**2. Literature Review**

Nowadays, there are many researchers applied MCDM models to any fields of sciences. A. Kengpol et al. [10] initially conducted a study on proposing a solar power plant location to avoid flooding in Thailand using geographical information system (GIS) to determine an optimum site condition. Upon the study, a number of geographical conditions had to be considered which developed into an MCDM problem of ranking the priorities amongs<sup>t</sup> the conditions using Fuzzy Analytic Hierarchy Process (FAHP) and the Technique of Order Preference by Similarity to Ideal Solution (TOPSIS).

A. Azadeh et al. [11] integrated a hierarchical model for solar plants' location selection by data envelopment analysis (DEA), principal component analysis (PCA) and numerical taxonomy (NT). Anuchit Thongpun et al. [12] focused on a building location selection approach for locating a solar power plant in Thailand based on DEA models. A. Azadeh et al. [13] applied an artificial neural network (ANN) and fuzzy data envelopment analysis (FDEA) for location optimization of solar plants with uncertainty and complexity.

Amy H. I. Lee et al. [14] integrated MCDM model to set the assurance region (AR) of the quantitative factors, and the AR is incorporated into data envelopment analysis (DEA), additionally adopting a fuzzy analytic hierarchy process (FAHP) for the location of a PV solar plant. Adnan Sozen et al. [15] presented an approach for the location of solar plants by data envelopment analysis (DEA). Ali Azadeh et al. [16] presented an integrated fuzzy DEA model for decision making on wind plant locations. Shinya Yokota et al. [17] applied data envelopment analysis (DEA) for the optimal allocation of mega-solar.

Chabuk et al. [18] discussed two MCDM methods to propose two alternative landfill sites for each area in the studied location, which were AHP and Ratio Scale Weighting (RSW), using multiple environmental factors as criteria for evaluation. The methods successfully proposed a geographical map that addressed the suitable locations throughout the studied sector. Chakraborty et al. [19] also proposed multiple MCDM methods including AHP for assigning criteria weights; Grey Rational Analysis (GRA), Multi Objective Optimization on the Basis of Ratio Analysis (MOORA), Elimination of Choice Translating Reality (ELECTRE II), and Operational Competitiveness Rating Analysis (OCRA) for ranking the results of each location alternative; and Spearman's rank correlation coe fficient, Kendall's rank correlation coe fficient, agreemen<sup>t</sup> between the top three ranked alternatives were used to compare each ranking methodology with another; finally, the REGIME was used for the final evaluation of each methodology after all the applied evaluating methodologies were calculated. With a huge comparison study between the methods, the author was able to utilize which methodology was most suitable for choosing the most suitable supplier.

Amy H. I. Lee et al. [20] proposed MCDM model to decide the most suitable photovoltaic solar plant allocation by using the interpretive structural modeling (ISM), the Serbian VlseKriterijumska OptimizacijaI Kompromisno Resenje (VIKOR), meaning multi-criteria optimization with a compromise solution, fuzzy analytic network process (FANP). A. Azadeh et al. [21] integrated hierarchical Data Envelopment Analysis for the location optimization of wind plants in Iran. This model would enable

the energy policy makers to select the best possible location for construction of a wind power plant with the lowest possible cost. Lei Fang et al. [22] applied the DEA model and goal programming to evaluate the relative e fficiency of each potential location.

Chia-Nan Wang et al. [9] proposed a similar MCDM approach to determine a solar power plant location for the entire country of Vietnam using the methods of DEA, FAHP, and TOPSIS to evaluate qualitative and quantitative criteria. Ehsan Dehghani et al. [23] evaluated di fferent areas for solar plants according to a set of social, geographical, and technical criteria through a data envelopment analysis (DEA) model. In this study, the DEA model considers both information of the e fficient and anti-e fficient frontiers to raise discrimination power in DEA analysis. Ali Mostafaeipour et al. [24] applied Data Envelopment Analysis (DEA) methodology to prioritize cities for installing the solar-hydrogen power plant so that one candidate location was selected for each city. A. Azadeh et al. [25] presented a technical and economic research for allocation of solar plants by using multivariable methods namely, Data Envelopment Analysis (DEA) and Principle Component Analysis (PCA). A hybrid model for the allocation of solar plants was presented by the utilization of most related parameters to solar plants and an integrated DEA-PCA approach. Seong Kon Lee et al. [26] proposed a hybrid model including a fuzzy Analytic Hierarchy Process (AHP)/Data Envelopment Analysis (DEA) for e fficiently allocating energy R&D resources in the case of energy technologies against high oil prices.

As the literature review shows, the amount of studies that apply the MCDM approach to various fields of science and engineering has increased in number in recent years. Location selection is one of the fields where the MCDM model has been employed, especially in the renewable energy sector, where decision makers must evaluate both qualitative and quantitative factors. Although some studies have reviewed the applications of MCDM approaches in solar power plant location selection, very few works have focused on this problem in a fuzzy environment. This is a reason why we proposed a fuzzy MCDM model in this study.
